Breast cancer detection project Report

INTRODUCTION:

    Breast cancer is a group of diseases in which cell in the breast tissue change and divide uncontrolled , typically resulting in a lump or mass. Most breast cancers begin in the lobules or in the ducts that connect the lobules to the nipple. It typically has no symptoms when the tumor is small and most easily treated, which is why screening is important for early detection.
    Breast cancer is typically detected either during screening, before symptoms have developed, or after a woman notices a lump. Most masses seen on a mammogram and most breast lumps turn out to be benign.According to WHO (World Health Organisation) in 2020, there were 2.3 million women diagnosed with breast cancer and 685000 deaths globally. As of the end of 2020, there were 7.8 million women alive who were diagnosed with breast cancer inn the past 5 years, making it the world's most prevalent cancer.
    Based on current incidence rates, 12.9% of women born in the United States today will develop breast cancer at some time during their lives. In Indian women breast cancer accounts for 14% of cancers. It is reported that with every four minutes, an Indian woman is diagnosed with breast cancer. Breast cancer is on the rise, both in rural and urban India.
    The doctors do not identify each and every breast cancer patient. That's the reason Machine Learning Engineer/ Data Scientist comes into the picture because they have knowledge of maths and computational power.
Some Risk Factorsfor Breast Cancer:
    The following are some of the known risk factors for breast cancer. How ever, most cases of breast cancer cannot be linked to a specific cause.

• Age -   The chance of getting breast cancer increases as women age. Nearly 80 percent of breast cancers are found in women over the age 45-50.
• Personal History of breast cancer -   A woman who has had breast cancer in one breast is at an increased risk of developing cancer in her other breast.
• Family history of breast cancer -   A woman has a higher risk of breast cancer if her mother, sister or daughter had breast cancer, especially at a young age (before 40). Having other relatives with breast cancer may also raise the risk.
• Genetic Factors -   Women with certain genetic mutations, including changes to the BRCA1 and Brca2 genes, are at higher risk of developing breast cancer during their lifetime. Other gene changes may raise breast cancer risk as well.
• Childbearing and menstrual history -   The older a woman is when she has her first child, the greater her risk of breast cancer. Also at higher risk are:

1. Women who menstruate for the first time at an early age (before 12).
2. women who go through menupause late (after age 55).
3. Women who've never had children.

Goal:

    We have extracted features of breast cancer patient cells and normal person cells. As Machine learning engineer/ Data Scientist has to create a Machine Learning model to classify "malignant (cancerous)" and "benign (not cancerous)" tumor. Since, the given dataset is a Supervised Learning data that supports Binary Classification. To achieve this I have used machine learning classification methods to fit a function that can predict the discrete class of new input.

Data Description:

Type:

    The given breast cancer dataset of the project has the labelled data means some of the input data [featured columns] is already tagged with the correct output data [ target column]. So, the given dataset is a Supervised dataset.
    In supervised learning, the training data provided to the machines work as the supervisor that teaches the machines to predict the output correctly. Supervised learning is a process of providing input data as well as correct output data to the machine learning model.

Columns:

    The given dataset has the feature columns and as well as the target columns:
Columns in the dataset :-
    'Unnamed: 0', 'id', 'diagnosis', 'radius_mean', 'texture_mean','perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean','concavity_mean', 'concave points_mean', 'symmetry_mean','fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se','area_se', 'smoothness_se', 'compactness_se', 'concavity_se','concave points_se', 'symmetry_se', 'fractal_dimension_se','radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst','smoothness_worst', 'compactness_worst', 'concavity_worst', 'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst'.

Useful Columns:

    In our given dataset only some columns are useful to build the machine learning model. The useful columns are divided into two types feature columns and target column.
Feature columns :-
    In the feature columns we used all the columns except some of unwanted columns ['Unnamed: 0', 'id'] and the target column ['diagnosis'].
Target Columns :-
    The 'daignosis' column is used as the target column. The diagnosis column indicates the patients who's cancer is Malignant or Benign.

Problem Statement:

    Breast Cancer is one of the leading cancer developed in many countries including India. Though the endurance rate is high - with early diagnosis 97% women can survive for more than 5 years. Statistically, the death toll due to this disease has increased drastically in last few decades. The main issue pertaining to its cure is early recognition. Hence, apart from medicinal solutiondd=s some Data Science solution needs to be integrated for resolving the death causing issue.
    This analysis aims to observe which features are most helpful in predicting malignant or benign cancer and to see general trends that may aid us in model selection and hyper parameter selection. The goal is to classify whether the breast cancer is benign or malignant. To achieve this I have used machine learning classification methods to fit a function that can predict the discrete class of new input.

Approach:

    In this project we will use Machine learning Algorithms to detect breast cancer, based off of data. We have the data to build the Machine Learning Model. For that we will apply Exploratory Data Analysis and Data Preprocessing techniques on our loaded Breast Cancer Dataset.
    We have clean dataset to build the machine learning model. But which Machine Learning Algorithmns should we use to build the machine learning model? As we discussed in the above sections our dataset is a Supervised Learning Dataset and our target column has two classes that means our dataset supports Binary-Class Classification. So, we use Supervised Learning Classification Algorithms in our project.
    Finally, After completion of the machine learning project or building the Machine Learning Model need to deploy in an application. To deploy the Machine Learning Model need to save using pickle package.

Proposed Solution And Result Analysis:

Import Essential Libraries:
    *We are importing 'numpy' library for numeric calculation in our project, 'pandas' library for data manupulation or analysis, 'matplotlib' and 'seaborn' libraries for data visualization. We are importing 'sklearn libraries' for training and testing the data and building Machine Learning Model as well. We are importing 'pickel' library to save the Machine Learning Model.

# Import essential libraries
import numpy as np # for numeric calculation
import pandas as pd # for data manupulation or analysis
import matplotlib.pyplot as plt # for data Visualization
import seaborn as sns # for data Visualization
from sklearn.model_selection import train_test_split # for splitting the data for training and testing 
from sklearn.preprocessing import StandardScaler # for converting different units and magnitude data in one unit
from sklearn.metrics import classification_report # for classification report of the ML Model
from sklearn.metrics import accuracy_score # to find the accurate score of ML model
from sklearn.metrics import confusion_matrix # to find the ML model is giving best output
from sklearn.svm import SVC # Support Vector Machine Classifier
from sklearn.linear_model import LogisticRegression # Logistic Regression Classifier
from sklearn.neighbors import KNeighborsClassifier # K-Nearest Neighbour Classifier
from sklearn.naive_bayes import GaussianNB # Naive Bayes Classifier
from sklearn.tree import DecisionTreeClassifier # Decision Tree Classifier
from sklearn.ensemble import RandomForestClassifier # Random Forest Classifier
from sklearn.ensemble import AdaBoostClassifier # AdaBoost Classifier
from sklearn.linear_model import SGDClassifier # Stochastic Gradient Descent Classifier
from sklearn.neural_network import MLPClassifier # Multi-Layer Perception Classifier
from sklearn.model_selection import cross_val_score # to find the ML model is overfitted, underfitted or generalize
import pickle # to save the machine learning model

Loading Dataset:
    We already downloaded breast cancer data in our device, we loading the dataset by use the pandas 'read_csv()'.

Cancer_dataset = pd.read_csv("breast_c_Dataset.csv")

Data Exploration:

    We are exploring the data in the dataset using some methods and functions to find the columns in the data, column names in the dataset,etc.,

#Exploring the Data in the Dataset
Cancer_dataset

	Unnamed: 0	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	...	25.380	17.33	184.60	2019.0	0.16220	0.66560	0.7119	0.2654	0.4601	0.11890
1	1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	...	24.990	23.41	158.80	1956.0	0.12380	0.18660	0.2416	0.1860	0.2750	0.08902
2	2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	...	23.570	25.53	152.50	1709.0	0.14440	0.42450	0.4504	0.2430	0.3613	0.08758
3	3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	...	14.910	26.50	98.87	567.7	0.20980	0.86630	0.6869	0.2575	0.6638	0.17300
4	4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	...	22.540	16.67	152.20	1575.0	0.13740	0.20500	0.4000	0.1625	0.2364	0.07678
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
564	564	926424	M	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	...	25.450	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115
565	565	926682	M	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	...	23.690	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637
566	566	926954	M	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	...	18.980	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820
567	567	927241	M	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	...	25.740	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400
568	568	92751	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	...	9.456	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039

569 rows × 33 columns

    The dataset has the 33 columns of 569 patients data that includes the patient-Id and diagnosis of the patients with another 30 columns of data.

#Type of the Dataset
print(type(Cancer_dataset))

<class 'pandas.core.frame.DataFrame'>

#Columns in the dataset
print(Cancer_dataset.keys())

Index(['Unnamed: 0', 'id', 'diagnosis', 'radius_mean', 'texture_mean',
       'perimeter_mean', 'area_mean', 'smoothness_mean', 'compactness_mean',
       'concavity_mean', 'concave points_mean', 'symmetry_mean',
       'fractal_dimension_mean', 'radius_se', 'texture_se', 'perimeter_se',
       'area_se', 'smoothness_se', 'compactness_se', 'concavity_se',
       'concave points_se', 'symmetry_se', 'fractal_dimension_se',
       'radius_worst', 'texture_worst', 'perimeter_worst', 'area_worst',
       'smoothness_worst', 'compactness_worst', 'concavity_worst',
       'concave points_worst', 'symmetry_worst', 'fractal_dimension_worst'],
      dtype='object')

#First 7 columns of the Dataset
Cancer_dataset.head(7)

	Unnamed: 0	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	...	25.38	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	...	24.99	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902
2	2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	...	23.57	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758
3	3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	...	14.91	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300
4	4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	...	22.54	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678
5	5	843786	M	12.45	15.70	82.57	477.1	0.12780	0.17000	0.1578	...	15.47	23.75	103.40	741.6	0.1791	0.5249	0.5355	0.1741	0.3985	0.12440
6	6	844359	M	18.25	19.98	119.60	1040.0	0.09463	0.10900	0.1127	...	22.88	27.66	153.20	1606.0	0.1442	0.2576	0.3784	0.1932	0.3063	0.08368

7 rows × 33 columns

#Last 7 columns of the Dataset
Cancer_dataset.tail(7)

	Unnamed: 0	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
562	562	925622	M	15.22	30.62	103.40	716.9	0.10480	0.20870	0.25500	...	17.520	42.79	128.70	915.0	0.14170	0.79170	1.1700	0.2356	0.4089	0.14090
563	563	926125	M	20.92	25.09	143.00	1347.0	0.10990	0.22360	0.31740	...	24.290	29.41	179.10	1819.0	0.14070	0.41860	0.6599	0.2542	0.2929	0.09873
564	564	926424	M	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	...	25.450	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115
565	565	926682	M	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	...	23.690	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637
566	566	926954	M	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	...	18.980	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820
567	567	927241	M	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	...	25.740	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400
568	568	92751	B	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	...	9.456	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039

7 rows × 33 columns

#Checking our target 'diagnosis' column
print(Cancer_dataset.diagnosis)

0      M
1      M
2      M
3      M
4      M
      ..
564    M
565    M
566    M
567    M
568    B
Name: diagnosis, Length: 569, dtype: object

    The diagnosis column is our target which contains the details of the patients who has the cancer and who doesn't havve cancer. That data in the diagnosis column is in the form of categorical format i.e. 'M' means 'malignant' and 'B' means 'benign'.

#Storing the diagnosis column into Target_Variables
Target_Variables = Cancer_dataset.diagnosis
print(Target_Variables)

0      M
1      M
2      M
3      M
4      M
      ..
564    M
565    M
566    M
567    M
568    B
Name: diagnosis, Length: 569, dtype: object

    Since,The data in the Target_Variables is a Categorical data, we need to Convert it into the numerical data. So that we can get accurate results While building Machine Learning Model.

#First we need to know how many unique values in the Target_Variables
print(Target_Variables.unique())

['M' 'B']

    There are two types of unique values in the Target_Variables they are 'M' ana 'B'. We are going to convert the categorical data into numerical data by giving 'M = 0' and 'B = 1'.

#Converting the Target_Variables data and storing the values in 't' array Variable
t = []
for i in Target_Variables:
    if(i == 'M'):
        t.append(0)
    else:
        t.append(1)       

#Assigning the t Values in Target
Target = t
print(Target)

[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1]

    Now, we have numerical data in the Target. We can easily use this data to build Machine Learning Model.
    Now, We are going to drop unwanted columns, diagnosis column and assign the remaining columns into 'Feature_Variables'.

#Feature_Variables
Feature_Variables = Cancer_dataset.drop(['Unnamed: 0', 'id', 'diagnosis'], axis = 1)
print(Feature_Variables)

     radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \
0          17.99         10.38          122.80     1001.0          0.11840   
1          20.57         17.77          132.90     1326.0          0.08474   
2          19.69         21.25          130.00     1203.0          0.10960   
3          11.42         20.38           77.58      386.1          0.14250   
4          20.29         14.34          135.10     1297.0          0.10030   
..           ...           ...             ...        ...              ...   
564        21.56         22.39          142.00     1479.0          0.11100   
565        20.13         28.25          131.20     1261.0          0.09780   
566        16.60         28.08          108.30      858.1          0.08455   
567        20.60         29.33          140.10     1265.0          0.11780   
568         7.76         24.54           47.92      181.0          0.05263   

     compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \
0             0.27760         0.30010              0.14710         0.2419   
1             0.07864         0.08690              0.07017         0.1812   
2             0.15990         0.19740              0.12790         0.2069   
3             0.28390         0.24140              0.10520         0.2597   
4             0.13280         0.19800              0.10430         0.1809   
..                ...             ...                  ...            ...   
564           0.11590         0.24390              0.13890         0.1726   
565           0.10340         0.14400              0.09791         0.1752   
566           0.10230         0.09251              0.05302         0.1590   
567           0.27700         0.35140              0.15200         0.2397   
568           0.04362         0.00000              0.00000         0.1587   

     fractal_dimension_mean  ...  radius_worst  texture_worst  \
0                   0.07871  ...        25.380          17.33   
1                   0.05667  ...        24.990          23.41   
2                   0.05999  ...        23.570          25.53   
3                   0.09744  ...        14.910          26.50   
4                   0.05883  ...        22.540          16.67   
..                      ...  ...           ...            ...   
564                 0.05623  ...        25.450          26.40   
565                 0.05533  ...        23.690          38.25   
566                 0.05648  ...        18.980          34.12   
567                 0.07016  ...        25.740          39.42   
568                 0.05884  ...         9.456          30.37   

     perimeter_worst  area_worst  smoothness_worst  compactness_worst  \
0             184.60      2019.0           0.16220            0.66560   
1             158.80      1956.0           0.12380            0.18660   
2             152.50      1709.0           0.14440            0.42450   
3              98.87       567.7           0.20980            0.86630   
4             152.20      1575.0           0.13740            0.20500   
..               ...         ...               ...                ...   
564           166.10      2027.0           0.14100            0.21130   
565           155.00      1731.0           0.11660            0.19220   
566           126.70      1124.0           0.11390            0.30940   
567           184.60      1821.0           0.16500            0.86810   
568            59.16       268.6           0.08996            0.06444   

     concavity_worst  concave points_worst  symmetry_worst  \
0             0.7119                0.2654          0.4601   
1             0.2416                0.1860          0.2750   
2             0.4504                0.2430          0.3613   
3             0.6869                0.2575          0.6638   
4             0.4000                0.1625          0.2364   
..               ...                   ...             ...   
564           0.4107                0.2216          0.2060   
565           0.3215                0.1628          0.2572   
566           0.3403                0.1418          0.2218   
567           0.9387                0.2650          0.4087   
568           0.0000                0.0000          0.2871   

     fractal_dimension_worst  
0                    0.11890  
1                    0.08902  
2                    0.08758  
3                    0.17300  
4                    0.07678  
..                       ...  
564                  0.07115  
565                  0.06637  
566                  0.07820  
567                  0.12400  
568                  0.07039  

[569 rows x 30 columns]

Creating a DataFrame:

    We are going to a Cancer Dataframe without losing our original dataset by concating 'FeatureVariables' and 'Target' with the help of pandas 'pd.DataFrame()' and for concatination of Target and Feature Variables we are using numpy 'np.c[x,y]'.

#Creating Cancer_df Dataframe
Cancer_df = pd.DataFrame(np.c_[Feature_Variables, Target], columns = np.append(Feature_Variables.columns, ['target']))
Cancer_df

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	target
0	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.30010	0.14710	0.2419	0.07871	...	17.33	184.60	2019.0	0.16220	0.66560	0.7119	0.2654	0.4601	0.11890	0.0
1	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.08690	0.07017	0.1812	0.05667	...	23.41	158.80	1956.0	0.12380	0.18660	0.2416	0.1860	0.2750	0.08902	0.0
2	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.19740	0.12790	0.2069	0.05999	...	25.53	152.50	1709.0	0.14440	0.42450	0.4504	0.2430	0.3613	0.08758	0.0
3	11.42	20.38	77.58	386.1	0.14250	0.28390	0.24140	0.10520	0.2597	0.09744	...	26.50	98.87	567.7	0.20980	0.86630	0.6869	0.2575	0.6638	0.17300	0.0
4	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.19800	0.10430	0.1809	0.05883	...	16.67	152.20	1575.0	0.13740	0.20500	0.4000	0.1625	0.2364	0.07678	0.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
564	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	0.1726	0.05623	...	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115	0.0
565	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	0.1752	0.05533	...	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637	0.0
566	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	0.1590	0.05648	...	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820	0.0
567	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	0.2397	0.07016	...	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400	0.0
568	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	0.1587	0.05884	...	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039	1.0

569 rows × 31 columns

#First 7 rows of the Cancer Dataframe
Cancer_df.head(7)

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	target
0	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	0.2419	0.07871	...	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890	0.0
1	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	0.1812	0.05667	...	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902	0.0
2	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	0.2069	0.05999	...	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758	0.0
3	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	0.2597	0.09744	...	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300	0.0
4	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	0.1809	0.05883	...	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678	0.0
5	12.45	15.70	82.57	477.1	0.12780	0.17000	0.1578	0.08089	0.2087	0.07613	...	23.75	103.40	741.6	0.1791	0.5249	0.5355	0.1741	0.3985	0.12440	0.0
6	18.25	19.98	119.60	1040.0	0.09463	0.10900	0.1127	0.07400	0.1794	0.05742	...	27.66	153.20	1606.0	0.1442	0.2576	0.3784	0.1932	0.3063	0.08368	0.0

7 rows × 31 columns

#Last 7 rows of the Cancer DataFrame
Cancer_df.tail(7)

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	target
562	15.22	30.62	103.40	716.9	0.10480	0.20870	0.25500	0.09429	0.2128	0.07152	...	42.79	128.70	915.0	0.14170	0.79170	1.1700	0.2356	0.4089	0.14090	0.0
563	20.92	25.09	143.00	1347.0	0.10990	0.22360	0.31740	0.14740	0.2149	0.06879	...	29.41	179.10	1819.0	0.14070	0.41860	0.6599	0.2542	0.2929	0.09873	0.0
564	21.56	22.39	142.00	1479.0	0.11100	0.11590	0.24390	0.13890	0.1726	0.05623	...	26.40	166.10	2027.0	0.14100	0.21130	0.4107	0.2216	0.2060	0.07115	0.0
565	20.13	28.25	131.20	1261.0	0.09780	0.10340	0.14400	0.09791	0.1752	0.05533	...	38.25	155.00	1731.0	0.11660	0.19220	0.3215	0.1628	0.2572	0.06637	0.0
566	16.60	28.08	108.30	858.1	0.08455	0.10230	0.09251	0.05302	0.1590	0.05648	...	34.12	126.70	1124.0	0.11390	0.30940	0.3403	0.1418	0.2218	0.07820	0.0
567	20.60	29.33	140.10	1265.0	0.11780	0.27700	0.35140	0.15200	0.2397	0.07016	...	39.42	184.60	1821.0	0.16500	0.86810	0.9387	0.2650	0.4087	0.12400	0.0
568	7.76	24.54	47.92	181.0	0.05263	0.04362	0.00000	0.00000	0.1587	0.05884	...	30.37	59.16	268.6	0.08996	0.06444	0.0000	0.0000	0.2871	0.07039	1.0

7 rows × 31 columns

#Columns of the Cancer DataFrame
Cancer_df.keys()

Index(['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',
       'smoothness_mean', 'compactness_mean', 'concavity_mean',
       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
       'fractal_dimension_se', 'radius_worst', 'texture_worst',
       'perimeter_worst', 'area_worst', 'smoothness_worst',
       'compactness_worst', 'concavity_worst', 'concave points_worst',
       'symmetry_worst', 'fractal_dimension_worst', 'target'],
      dtype='object')

#Details of the Cancer DataFrame
Cancer_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 31 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   radius_mean              569 non-null    float64
 1   texture_mean             569 non-null    float64
 2   perimeter_mean           569 non-null    float64
 3   area_mean                569 non-null    float64
 4   smoothness_mean          569 non-null    float64
 5   compactness_mean         569 non-null    float64
 6   concavity_mean           569 non-null    float64
 7   concave points_mean      569 non-null    float64
 8   symmetry_mean            569 non-null    float64
 9   fractal_dimension_mean   569 non-null    float64
 10  radius_se                569 non-null    float64
 11  texture_se               569 non-null    float64
 12  perimeter_se             569 non-null    float64
 13  area_se                  569 non-null    float64
 14  smoothness_se            569 non-null    float64
 15  compactness_se           569 non-null    float64
 16  concavity_se             569 non-null    float64
 17  concave points_se        569 non-null    float64
 18  symmetry_se              569 non-null    float64
 19  fractal_dimension_se     569 non-null    float64
 20  radius_worst             569 non-null    float64
 21  texture_worst            569 non-null    float64
 22  perimeter_worst          569 non-null    float64
 23  area_worst               569 non-null    float64
 24  smoothness_worst         569 non-null    float64
 25  compactness_worst        569 non-null    float64
 26  concavity_worst          569 non-null    float64
 27  concave points_worst     569 non-null    float64
 28  symmetry_worst           569 non-null    float64
 29  fractal_dimension_worst  569 non-null    float64
 30  target                   569 non-null    float64
dtypes: float64(31)
memory usage: 137.9 KB

    We are going to find if there are any null Values in the Cancer Dataframe.

#Checkin for null values
print(Cancer_df.isnull().sum())

radius_mean                0
texture_mean               0
perimeter_mean             0
area_mean                  0
smoothness_mean            0
compactness_mean           0
concavity_mean             0
concave points_mean        0
symmetry_mean              0
fractal_dimension_mean     0
radius_se                  0
texture_se                 0
perimeter_se               0
area_se                    0
smoothness_se              0
compactness_se             0
concavity_se               0
concave points_se          0
symmetry_se                0
fractal_dimension_se       0
radius_worst               0
texture_worst              0
perimeter_worst            0
area_worst                 0
smoothness_worst           0
compactness_worst          0
concavity_worst            0
concave points_worst       0
symmetry_worst             0
fractal_dimension_worst    0
target                     0
dtype: int64

    After checking for the null values we got no null values in the Cancer Dataframe with the 'datatype: int64'.

    We are going to find the count, mean, standard deviation, minimum, maximum, 25%, 50% and 75% values of the Cancer Dataset.

Cancer_df.describe()

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	target
count	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	...	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000
mean	14.127292	19.289649	91.969033	654.889104	0.096360	0.104341	0.088799	0.048919	0.181162	0.062798	...	25.677223	107.261213	880.583128	0.132369	0.254265	0.272188	0.114606	0.290076	0.083946	0.627417
std	3.524049	4.301036	24.298981	351.914129	0.014064	0.052813	0.079720	0.038803	0.027414	0.007060	...	6.146258	33.602542	569.356993	0.022832	0.157336	0.208624	0.065732	0.061867	0.018061	0.483918
min	6.981000	9.710000	43.790000	143.500000	0.052630	0.019380	0.000000	0.000000	0.106000	0.049960	...	12.020000	50.410000	185.200000	0.071170	0.027290	0.000000	0.000000	0.156500	0.055040	0.000000
25%	11.700000	16.170000	75.170000	420.300000	0.086370	0.064920	0.029560	0.020310	0.161900	0.057700	...	21.080000	84.110000	515.300000	0.116600	0.147200	0.114500	0.064930	0.250400	0.071460	0.000000
50%	13.370000	18.840000	86.240000	551.100000	0.095870	0.092630	0.061540	0.033500	0.179200	0.061540	...	25.410000	97.660000	686.500000	0.131300	0.211900	0.226700	0.099930	0.282200	0.080040	1.000000
75%	15.780000	21.800000	104.100000	782.700000	0.105300	0.130400	0.130700	0.074000	0.195700	0.066120	...	29.720000	125.400000	1084.000000	0.146000	0.339100	0.382900	0.161400	0.317900	0.092080	1.000000
max	28.110000	39.280000	188.500000	2501.000000	0.163400	0.345400	0.426800	0.201200	0.304000	0.097440	...	49.540000	251.200000	4254.000000	0.222600	1.058000	1.252000	0.291000	0.663800	0.207500	1.000000

8 rows × 31 columns

    If we see the count all the columns has 569 values means there are no missing Values in the Cancer Dataset.

Data Visualization:

Pairplot of the Cancer DataFrame:
    The pair plot is used to show the numeric distribution in the scatter plot.

#Pairplot of the Cancer_df
sns.set()
plt.figure(figsize = (12,12))
sns.pairplot(Cancer_df, hue = "target")
plt.show()

<Figure size 864x864 with 0 Axes>

    For pairplot we gave the hue as "target" to find the numeric distribution of the Cancer_df with target values.

#Pairplot of the sample features in the Cancer DataFrame
sns.set()
plt.figure(figsize = (12,12))
sns.pairplot(Cancer_df, hue = 'target', vars = ['radius_mean', 'texture_mean', 'perimeter_mean', 'area_mean',
       'smoothness_mean', 'compactness_mean', 'concavity_mean'])
plt.show()

<Figure size 864x864 with 0 Axes>

    The pair plot showing malignant and benign tumor data distributed in two classes. It is easy to differentiate in the pair plot.

Counterplot of the Target:
    Showing the total count of malignant and benign tumor patients in counterplot.

#Counterplot of target:
sns.set()
plt.figure(figsize = (20,6))
sns.countplot(Cancer_df['target'])
plt.show()

C:\Users\jampa\anaconda3\anaconda\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

    In the counterplot the maximum samples is equal to 1, that means the non cancerous patients are maximum.

#Counterplot of feature mean radius
sns.set()
plt.figure(figsize = (20,8))
sns.countplot(Cancer_df['radius_mean'])
plt.show()

C:\Users\jampa\anaconda3\anaconda\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

    The above counterplot has the maximum samples of mean radius is equal to 1.

Heatmap of Cancer DataFrame:

#Heatmap of Cancer Dataframe
sns.set()
plt.figure(figsize = (16,9))
sns.heatmap(Cancer_df)
plt.show()

    In the above Heatmap the area_mean and area_worst are greater than the other features. perimeter_mean, area_se and perimeter_worst are slightly greater than other features.

Heatmap of Correlation matrix:
    To find a Correlation between each feature and target we visualize heatmap using the correlation matrix.

# heatmap of correlation matrix
sns.set()
plt.figure(figsize = (16,9))
corel = Cancer_df.corr()
sns.heatmap(corel, annot = True, xticklabels = corel.columns, yticklabels = corel.columns, cmap = 'YlGnBu', linewidth = 2)
plt.show()

Boxplot of Cancer DataFrame:
    To find the outliers in the breast cancer dataframe we use boxplot.

# Boxplot for the cancer dataframe
sns.set()
plt.figure(figsize = (10,7))
Cancer_df.boxplot()
plt.xticks(rotation = 90)
plt.show()

    In the above boxplot the outliers are greater at area_worst, area_mean and also there are some outliers at area_se, perimeter_mean and perimeter_worst.

Correlation Barplot:
    We are taking the correlation of each feature with target and visualize the barplot. For that, We are going to create the another Cancer DataFrame by droping the target column.

#Creating the Dataframe by droping the target
Cancer_df2 = Cancer_df.drop(['target'], axis = 1)

#Visualize the Correlation barplot
sns.set()
plt.figure(figsize = (16,9))
ax = sns.barplot(Cancer_df2.corrwith(Cancer_df.target).index, Cancer_df2.corrwith(Cancer_df.target))
ax.tick_params(labelrotation = 90)

C:\Users\jampa\anaconda3\anaconda\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  warnings.warn(

    In the above correlation barplot only 'smoothness_se' is strongly positively correlated with the target than others. The features 'fractal_dimension_mean', 'texture_se', 'symmetry_se' are less positively correlated with target and others are strongly negatively correlated with target.

Data Preprocessing:

Split Cancer Dataframe in train and test:

    For train and test split we are dividing the Dataframe into the Input Variable (X) and output Variable (y).

#input variable
X = Cancer_df2
X.head(7)

	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	fractal_dimension_mean	...	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst
0	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	0.2419	0.07871	...	25.38	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890
1	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	0.1812	0.05667	...	24.99	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902
2	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	0.2069	0.05999	...	23.57	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758
3	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	0.2597	0.09744	...	14.91	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300
4	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	0.1809	0.05883	...	22.54	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678
5	12.45	15.70	82.57	477.1	0.12780	0.17000	0.1578	0.08089	0.2087	0.07613	...	15.47	23.75	103.40	741.6	0.1791	0.5249	0.5355	0.1741	0.3985	0.12440
6	18.25	19.98	119.60	1040.0	0.09463	0.10900	0.1127	0.07400	0.1794	0.05742	...	22.88	27.66	153.20	1606.0	0.1442	0.2576	0.3784	0.1932	0.3063	0.08368

7 rows × 30 columns

#output variable
y = Cancer_df['target']
y.head()

0    0.0
1    0.0
2    0.0
3    0.0
4    0.0
Name: target, dtype: float64

# split dataset into train and test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 2)

    We divided the Cancer DataFrame the train data with '0.8' and test data with '0.2' and the random state is '2'.

#print the shape of the train and test data of input and output variable
print("shape of the X_train - ", X_train.shape)
print("shape of the X_test - ", X_test.shape)
print("shape of the y_train - ", y_train.shape)
print("shape of the y_test - ", y_test.shape)

shape of the X_train -  (455, 30)
shape of the X_test -  (114, 30)
shape of the y_train -  (455,)
shape of the y_test -  (114,)

Feature Scaling:
    We are feature scaling the Cancer DataFrame to convert different units and magnitude data in one unit.

#loading Standard Scaler Function
scaler = StandardScaler()

# fitting and tranforming the data
X_train_sc = scaler.fit_transform(X_train)
X_test_sc = scaler.fit_transform(X_test)

Breast Cancer Detection Machine Learning Model Building:

    We have clean data to build the Machine Learning model. Since, our output variable is a categorical data so we use supervised classification machine learning algorithms. To build the best model, we have to train and test the dataset with multiple machine learning algorithms then we can find the best machine learning model.

ACCURACY SCORE OF THE DATASET WITH MULTIPLE MODELS:-

Support Vector Classifier:

#Trained with data:
svc_classifier = SVC()
svc_classifier.fit(X_train, y_train)
y_pred_svc = svc_classifier.predict(X_test)
print("Accuracy score of Data with Support Vector Classifier - ", accuracy_score(y_test,y_pred_svc))
# Train with Standard Scaled Data
svc_classifier.fit(X_train_sc, y_train)
y_pred_svc_sc = svc_classifier.predict(X_test_sc)
print("Accuracy score of Standard Scaled Data with Support Vector Classifier - ", accuracy_score(y_test, y_pred_svc_sc))

Accuracy score of Data with Support Vector Classifier -  0.9035087719298246
Accuracy score of Standard Scaled Data with Support Vector Classifier -  0.9736842105263158

    By using 'Support Vector Classifier Algorithm' we getting the results:

• Trained with data gives the accuracy score - 90.35
• Trained with Standard Scaled Data gives the accuracy score - 97.36

Logistic Regression Classifier:

#Trained with data:
lr_classifier = LogisticRegression(random_state = 40, penalty = 'l2', max_iter = 100, solver = 'lbfgs')
lr_classifier.fit(X_train, y_train)
y_pred_lr = lr_classifier.predict(X_test)
print("Accuracy score of Data with Logistic Regression Classifier - ", accuracy_score(y_test, y_pred_lr))
#Trained with Standard Scaled Data:
lr_classifier.fit(X_train_sc, y_train)
y_pred_lr_sc = lr_classifier.predict(X_test_sc)
print("Accuracy score of Standard Scaled Data with Logistic Regression Classifier - ", accuracy_score(y_test, y_pred_lr_sc))

Accuracy score of Data with Logistic Regression Classifier -  0.9298245614035088
Accuracy score of Standard Scaled Data with Logistic Regression Classifier -  0.9736842105263158

C:\Users\jampa\anaconda3\anaconda\lib\site-packages\sklearn\linear_model\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

    By using 'Logistic regression Classifier Algorithm' we getting the results:

• Trained with data gives the accuracy score - 92.98
• Trained with Standard Scaled Data gives the accuracy score - 97.36

K-Nearest Neighbour Classifier:

#Trained with data:
knn_classifier = KNeighborsClassifier(n_neighbors = 3, metric = 'minkowski', p = 2)
knn_classifier.fit(X_train, y_train)
y_pred_knn = knn_classifier.predict(X_test)
print("Accuracy score of Data with K-Nearest Neighbour Classifier - ", accuracy_score(y_test, y_pred_knn))
#Trained with Standard Scaled data:
knn_classifier2 = KNeighborsClassifier(n_neighbors = 3, metric = 'minkowski', p = 2)
knn_classifier2.fit(X_train_sc, y_train)
y_pred_knn_sc = knn_classifier.predict(X_test_sc)
print("Accuracy score of Standard Scaled Data with K-Nearest Neighbour Classifier - ", accuracy_score(y_test, y_pred_knn_sc))

Accuracy score of Data with K-Nearest Neighbour Classifier -  0.9035087719298246
Accuracy score of Standard Scaled Data with K-Nearest Neighbour Classifier -  0.6052631578947368

    By using 'K-Nearest Neighbour Classifier Algorithm' we getting the results:

• Trained with data gives the accuracy score - 90.35
• Trained with Standard Scaled Data gives the accuracy score - 60.52

Naive Bayes Classifier:

#Trained with data
nb_classifier = GaussianNB()
nb_classifier.fit(X_train, y_train)
y_pred_nb = nb_classifier.predict(X_test)
print("Accuracy score of Data with Naive Bayes Classifier - ", accuracy_score(y_test, y_pred_nb))
#Trained with Standard Scaled Data
nb_classifier.fit(X_train_sc, y_train)
y_pred_nb_sc= nb_classifier.predict(X_test_sc)
print("Accuracy score of Standard Scaled Data with Naive Bayes Classifier - ", accuracy_score(y_test, y_pred_nb_sc))

Accuracy score of Data with Naive Bayes Classifier -  0.9385964912280702
Accuracy score of Standard Scaled Data with Naive Bayes Classifier -  0.9473684210526315

    By using 'Naive Bayes Classifier Algorithm' we getting the results:

• Trained with data gives the accuracy score - 93.85
• Trained with Standard Scaled Data gives the accuracy score - 94.73

Decision Tree Classifier:

#Trained with data
dt_classifier = DecisionTreeClassifier(criterion = 'entropy', random_state = 40)
dt_classifier.fit(X_train, y_train)
y_pred_dt = dt_classifier.predict(X_test)
print("Accuracy score of Data with Decision Tree Classifier - ", accuracy_score(y_test, y_pred_dt))
#Trained with Standard Scaled Data
dt_classifier.fit(X_train_sc, y_train)
y_pred_dt_sc = dt_classifier.predict(X_test_sc)
print("Accuracy score of Standard Scaled Data with Decision Tree Classifier - ", accuracy_score(y_test, y_pred_dt_sc))

Accuracy score of Data with Decision Tree Classifier -  0.9210526315789473
Accuracy score of Standard Scaled Data with Decision Tree Classifier -  0.9210526315789473

    By using 'Decision Tree Classifier Algorithm' we getting the results:

• Trained with data gives the accuracy score - 92.10
• Trained with Standard Scaled Data gives the accuracy score - 92.10

Random Forest Classifier:

#Trained with data
rf_classifier = RandomForestClassifier(n_estimators = 20, criterion = 'entropy', random_state = 40)
rf_classifier.fit(X_train, y_train)
y_pred_rf = rf_classifier.predict(X_test)
print("Accuracy score of Data with Random Forest Classifier - ", accuracy_score(y_test, y_pred_rf))
#Trained with Standard Scaled Data
rf_classifier.fit(X_train_sc, y_train)
y_pred_rf_sc = rf_classifier.predict(X_test_sc)
print("Accuracy score of Standard Scaled Data with Random Forest Classifier - ", accuracy_score(y_test, y_pred_rf_sc))

Accuracy score of Data with Random Forest Classifier -  0.9385964912280702
Accuracy score of Standard Scaled Data with Random Forest Classifier -  0.9473684210526315

    By using 'Random Forest Classifier Algorithm' we getting the results:

• Trained with data gives the accuracy score - 93.85
• Trained with Standard Scaled Data gives the accuracy score - 94.73

AdaBoost Classifier:

#Trained with data
adb_classifier = AdaBoostClassifier(DecisionTreeClassifier(criterion = 'entropy', random_state = 200),n_estimators = 2000, learning_rate = 0.1, algorithm = 'SAMME.R', random_state = 1)
adb_classifier.fit(X_train, y_train)
y_pred_adb = adb_classifier.predict(X_test)
print("Accuracy score of Data with AdaBoost Classifier - ", accuracy_score(y_test,y_pred_adb))
#Trained with Standard Scaled Data
adb_classifier.fit(X_train_sc, y_train)
y_pred_adb_sc = adb_classifier.predict(X_test_sc)
print("Accuracy score of Standard Scaled Data with AdaBoost Classifier - ", accuracy_score(y_test, y_pred_adb_sc))

Accuracy score of Data with AdaBoost Classifier -  0.9122807017543859
Accuracy score of Standard Scaled Data with AdaBoost Classifier -  0.9122807017543859

    By using 'AdaBoost Classifier Algorithm' we getting the results:

• Trained with data gives the accuracy score - 91.22
• Trained with Standard Scaled Data gives the accuracy score - 91.22

Stochastic Gradient Descent Classifier:

#Trained with data
sgd_classifier = SGDClassifier(max_iter = 1000, tol = 1e-3)
sgd_classifier.fit(X_train, y_train)
y_pred_sgd = sgd_classifier.predict(X_test)
print("Accuracy score of Data with Stochastic Gradient Descent Classifier - ", accuracy_score(y_test, y_pred_sgd))
#Trained with Standard Scaled Data
sgd_classifier.fit(X_train_sc, y_train)
y_pred_sgb_sc = sgd_classifier.predict(X_test_sc)
print("Accuracy score of Standard Scaled Data with Stochastic Gradient Descent Classifier - ", accuracy_score(y_test, y_pred_sgb_sc))

Accuracy score of Data with Stochastic Gradient Descent Classifier -  0.868421052631579
Accuracy score of Standard Scaled Data with Stochastic Gradient Descent Classifier -  0.9649122807017544

    By using 'Stochastic Gradient Descent Classifier Algorithm' we getting the results:

• Trained with data gives the accuracy score - 86.84
• Trained with Standard Scaled Data gives the accuracy score - 96.49

Multi-Layer Preceptron Classifier:

#Trained with data
mlp_classifier = MLPClassifier(solver = 'lbfgs', alpha = 1e-5, hidden_layer_sizes = (5, 2), random_state = 1)
mlp_classifier.fit(X_train, y_train)
y_pred_mlp = mlp_classifier.predict(X_test)
print("Accuracy score of Data with Multi-Layer Preceptron Classifier - ", accuracy_score(y_test, y_pred_mlp))
#Trained with Standard Scaled Data
mlp_classifier.fit(X_train_sc, y_train)
y_pred_mlp_sc = mlp_classifier.predict(X_test_sc)
print("Accuracy score of Standard Scaled Data with Multi-Layer Preceptron Classifier - ", accuracy_score(y_test, y_pred_mlp_sc))

Accuracy score of Data with Multi-Layer Preceptron Classifier -  0.9035087719298246
Accuracy score of Standard Scaled Data with Multi-Layer Preceptron Classifier -  0.9649122807017544

C:\Users\jampa\anaconda3\anaconda\lib\site-packages\sklearn\neural_network\_multilayer_perceptron.py:500: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
  self.n_iter_ = _check_optimize_result("lbfgs", opt_res, self.max_iter)

    By using 'Multi-Layer Preceptron Classifier Algorithm' we getting the results:

• Trained with data gives the accuracy score - 90.35
• Trained with Standard Scaled Data gives the accuracy score - 96.49

    By observing accuracy scores of all the Machine learning models 'Logistic Regression' is giving the highest accuracy score with Standard Scaled data by comparing with other models. Eventhough the 'SGD Model' is giving highest accuracy score but the accuracy score has been changing gradully with the same parameters. So, We choose 'Logistic Regression Classifier' to build the machine learning model.

CONFUSION MATRIX:

# CONFUSION MATRIX of the Dataframe in LOGISTIC REGRESSION
cm = confusion_matrix(y_test, y_pred_lr_sc)
print(cm)
sns.set()
plt.title('Heatmap of Confusion Matrix Logistic Regression Classifier', fontsize = 15)
sns.heatmap(cm, annot = True)
plt.show()

[[42  3]
 [ 0 69]]

    This model is giving 0% type II error and it is the best.

CLASSIFICATION REPORT OF MODEL:

# CLASSIFICATION REPORT of Logistic Regression classifier
print('classification report of Logistic Regression Classifier')
print(classification_report(y_test, y_pred_lr_sc))

classification report of Logistic Regression Classifier
              precision    recall  f1-score   support

         0.0       1.00      0.93      0.97        45
         1.0       0.96      1.00      0.98        69

    accuracy                           0.97       114
   macro avg       0.98      0.97      0.97       114
weighted avg       0.97      0.97      0.97       114

CROSS-VALIDATION OF THE MACHINE LEARNING MODEL:

    To find the Machine Learning model is overfitted, underfitted or generalized doing cross-Validation.

# CROSS VALIDATION OF THE MACHINE LEARNING MODEL
cross_validation = cross_val_score(estimator = lr_classifier, X = X_train_sc, y = y_train, cv = 10)
print("Cross validation accuracy of Linear Regression Model = ", cross_validation)
print("\nCross validation mean accuracy of Linear Regression Model = ", cross_validation.mean())

Cross validation accuracy of Linear Regression Model =  [1.         0.93478261 0.97826087 1.         0.97826087 0.97777778
 0.97777778 0.97777778 1.         0.95555556]

Cross validation mean accuracy of Linear Regression Model =  0.9780193236714976

    The mean accuracy value of Cross-Validation is 97.80% and Logistic Regression model accuracy is 97.36%. It showing Logistic Regression Model is slightly underfitted but when training data it will generalized model.

Save the Machine Learning Moodel:

    After completion of the building Machine Learning model, it needs to deploy in an application. To deploy the Machine learning model need to save it first. Too save the Machine Learning project we are using 'Pickle' package.

# SAVE THE MACHINE LEARNING MODEL

# save model
pickle.dump(lr_classifier, open('breast_cancer_detector.pickle', 'wb'))

#load model
breast_cancer_detector_model = pickle.load(open('breast_cancer_detector.pickle', 'rb'))

#predict the output
y_pred = breast_cancer_detector_model.predict(X_test_sc)

#confusion matrix
print('Confusion matrix of Logistic Regression: \n', confusion_matrix(y_test, y_pred),'\n')

#accuracy score
print('Accuracy of Logistic Regression = ', accuracy_score(y_test, y_pred))

Confusion matrix of Logistic Regression: 
 [[42  3]
 [ 0 69]] 

Accuracy of Logistic Regression =  0.9736842105263158

    We have completed the building and saving the Machine Learning Model with 97.36% accuracy rate.

Conclusion:

    We trained all the Supervised Classification Algorithmns to get more accuracy in the results. After training all the Algorithms, we found that 'Support Vector Machine', 'Logistic Regression', 'Stochastic Gradient Descent' and 'Multi-Layer Preceptron' are giving highest accuracy rate, but we choose 'Logistic Regression' as our Algorithm to build this Machine Learning Project. It was giving 97.36% accuracy rate with slightly underfitting with '97.80%' when Cross-Validate.

References:

• https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/breast-cancer-facts-and-figures/breast-cancer-facts-and-figures-2019-2020
• https://www.who.int/news-room/fact-sheets/detail/breast-cancer#:~:text=In%202020%2C%20there%20were%202.3,the%20world's%20most%20prevalent%20cancer
• https://www.cancer.gov/types/breast/risk-fact-sheet
• https://cytecare.com/blog/statistics-of-breast-cancer/#:~:text=With%20being%20the%20most%20common,in%20rural%20and%20urban%20India
• https://www.researchgate.net/publication/342303246_Breast_Cancer_Detection_and_Prediction_using_Machine_Learning
• https://indianaiproduction.com/breast-cancer-detection-using-machine-learning-classifier
• https://www.javatpoint.com/supervised-machine-learning
• https://numpy.org/doc/stable/numpy-user.pdf
• https://pandas.pydata.org/docs/pandas.pdf
• https://matplotlib.org/stable/Matplotlib.pdf
• https://seaborn.pydata.org/tutorial.html
• https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
• https://scikit-learn.org/stable/modules/model_evaluation.html
• https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
• https://scikit-learn.org/stable/modules/preprocessing.html
• https://scikit-learn.org/stable/modules/linear_model.html
• https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
• https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
• https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
• https://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.GaussianNB.html
• https://scikit-learn.org/stable/modules/ensemble.html
• https://scikit-learn.org/stable/modules/neural_networks_supervised.html
• https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.cross_val_score.html
• https://docs.python.org/3/library/pickle.html